Statistical Analysis of the DN/dS Ratio in DNA Sequences: Structure, Function and Evolution.

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By identifying natural selection at the level of DNA sequences, it possible to address questions regarding molecular evolution and to identify genomic regions or sites in the DNA sequence of special functional importance. In this project new statistical methods for identifying and interpreting patterns of selection will be developed. A Bayesian method based on Markov chain Monte Carlo (MCMC) will be developed that will provide a more powerful and versatile tool for identifying selection at the level of the DNA sequence. Methods for estimating the age of a mutation, for estimating the distribution of selection coefficients and for detecting correlated evolution in DNA sequences will also be developedThe new methods will be applied to several data sets, particularly data sets of viral sequences such as HIV-1 sequences. One of the questions that will be addressed is how often compensatory mutations occur in the evolution of drug resistance in the HIV-1 virus. The new methods will be applicable in many genomic studies, particularly for identifying regions or sites of functional importance.